In this paper, we describe an efficient implementation of the 3D SIFT-Rank algorithm via GPU parallelization of the operations required to generate the Gaussian scale-space. In particular, we target the Gaussian convolution, difference of Gaussians, sub-sampling and 4D extraction. This work was the first published GPU implementation of the 3D SIFT algorithm, first validated in the context of brain image indexing [1]}. Previously, GPU processing was used to speed up the SIFT algorithm for 2D image data [2]}, in real-time [3]} and video processing contexts [4]}, and high-dimensional feature matching [5]} applications, and for use on mobile devices [6]}. We here provide a detailed descriptions of the implementation, justifying our parameter choices based on the CUDA API, and describing our optimisation strategy and its effect on speed. We also describe and test computationally efficient 3D descriptors including the Binary Robust Independent Elementary Features (BRIEF) approach of Calonder et al. [7]}, and a propose a novel descriptor based on BRIEF that we call RRIEF (Ranked Robust Independent Elementary Features). These descriptors are compared to the 3D SIFT-Rank descriptor of Toews and Wells [8]}, [9]} in keypoint matching experiments of magnetic resonance image (MRI)s of the human brain.
